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Combination of GNN and MNL: a new model for dealing with multi-classification tasks

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27 lis 2024

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J. D. Pineda-Jaramillo, “A review of machine learning (ml) algorithms used for modeling travel mode choice,” Dyna, vol. 86, no. 211, pp. 32–41, 2019.Search in Google Scholar

H. C. Sox, M. C. Higgins, D. K. Owens, and G. S. Schmidler, Medical decision making. John Wiley & Sons, 2024.Search in Google Scholar

P. Domingos, “A few useful things to know about machine learning,” Communications of the ACM, vol. 55, no. 10, pp. 78–87, 2012.Search in Google Scholar

F. Rosenblatt, “The perceptron: a probabilistic model for information storage and organization in the brain.” Psychological review, vol. 65, no. 6, p. 386, 1958.Search in Google Scholar

R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936.Search in Google Scholar

C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, pp. 273–297, 1995.Search in Google Scholar

V. Vapnik, “” an overview of statistical learning theory”, ieee transactions on neural networks, vol. 10,№ 5,” 1999.Search in Google Scholar

C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, pp. 1–27, 2011.Search in Google Scholar

W. H. Greene and D. A. Hensher, “A latent class model for discrete choice analysis: contrasts with mixed logit,” Transportation Research Part B: Methodological, vol. 37, no. 8, pp. 681–698, 2003.Search in Google Scholar

A. Kendall, Y. Gal, and R. Cipolla, “Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7482–7491.Search in Google Scholar

L. Grigolon and F. Verboven, “Nested logit or random coefficients logit? a comparison of alternative discrete choice models of product differentiation,” Review of Economics and Statistics, vol. 96, no. 5, pp. 916–935, 2014.Search in Google Scholar

D. A. Hensher and T. T. Ton, “A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice,” Transportation Research Part E: Logistics and Transportation Review, vol. 36, no. 3, pp. 155–172, 2000.Search in Google Scholar

T. Kim, X. Zhou, and R. M. Pendyala, “Computational graph-based framework for integrating econometric models and machine learning algorithms in emerging data-driven analytical environments,” Transportmetrica A: Transport Science, vol. 18, no. 3, pp. 1346–1375, 2022.Search in Google Scholar

J. Ma, X. Ye, K. Huang et al., “Development of integrated choice and latent variable (iclv) models using matrix-based analytic approximation and automatic differentiation methods on tensorflow platform,” Journal of Advanced Transportation, vol. 2022, 2022.Search in Google Scholar

C. Bishop, “Neural networks for pattern recognition,” Clarendon Press google schola, vol. 2, pp. 223–228, 1995.Search in Google Scholar

S. Wang, Q. Wang, and J. Zhao, “Multitask learning deep neural networks to combine revealed and stated preference data,” Journal of choice modelling, vol. 37, p. 100236, 2020.Search in Google Scholar

B. Sifringer, V. Lurkin, and A. Alahi, “Enhancing discrete choice models with representation learning,” Transportation Research Part B: Methodological, vol. 140, pp. 236–261, 2020.Search in Google Scholar

S. Wu, F. Sun, W. Zhang, X. Xie, and B. Cui, “Graph neural networks in recommender systems: a survey,” ACM Computing Surveys, vol. 55, no. 5, pp. 1–37, 2022.Search in Google Scholar

B. Li, J. Tang, Y. Zhang, and X. Xie, “Ensemble of the deep convolutional network for multiclass of plant disease classification using leaf images,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 04, p. 2250016, 2022.Search in Google Scholar

Z.-M. Chen, X.-S. Wei, P. Wang, and Y. Guo, “Multi-label image recognition with graph convolutional networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 5177–5186.Search in Google Scholar

A. Anas, “The estimation of multinomial logit models of joint location and travel mode choice from aggregated data,” Journal of regional science, vol. 21, no. 2, pp. 223–242, 1981.Search in Google Scholar

M. Vrtic and K. W. Axhausen, “The impact of tilting trains in switzerland: A route choice model of regional-and long distance public transport trips,” Arbeitsberichte Verkehrs-und Raumplanung, vol. 128, 2002.Search in Google Scholar

J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A review on generative adversarial networks: Algorithms, theory, and applications,” IEEE transactions on knowledge and data engineering, vol. 35, no. 4, pp. 3313–3332, 2021.Search in Google Scholar

Język:
Angielski
Częstotliwość wydawania:
1 razy w roku
Dziedziny czasopisma:
Nauki biologiczne, Nauki biologiczne, inne, Matematyka, Matematyka stosowana, Matematyka ogólna, Fizyka, Fizyka, inne